PhD Candidates

PhD candidates and subjects (May 2020)

The following data are published in accordance to paragraph  4, article 39, of regulation 4485/2017:

The names of the PhD candidates and the members of the advisory committees, are published together with the titles and abstracts of the dissertations, on the Institution’s website, both in Greek and English.


PhD Candidate: Vassilios D. Vassios

Supervisor:
Dimitrios K. Papakostas, Professor IHU

Advisory Committee:
Alkiviadis Hatzopoulos, Professor AUTH
Argirios Hatzopoulos, Assistant Professor IHU

Title:
Fault Testing Methods and Algorithms for Analog and Mixed-Mode Electronic Circuits using Embedded Systems

Summary:
The scope of the PhD Thesis is to develop novel methods and algorithms for testing faults in analog and mixed-mode electronic circuits. The primary methodology that will be followed, consists of data gathered from the measurements of signal response of non-faulty electronic circuits and compared against similar measurements from circuits that are injected with selected faults. The aim of this research is to try to develop new metrics that will help the improve the efficiency of the developed algorithms and try to improve previous algorithms regarding the classification of the Circuit Under Test (CUT) to Good/Faulty by decreasing the percentage of the classified CUTs to False Positive (Faulty) or False Negative (Good). This algorithm will be implemented in an embedded system (μCU,FPGA) with main goal to improve the classification timing of the CUT’s to Good/Faulty.


PhD Candidate: Georgios Gravanis

Supervisor:
Konstantinos Diamantaras, Professor IHU

Advisory Committee:
Simira Papadopoulou, Professor IHU
Michail Salampasis, Professor IHU

Title:
Fault Detection in industrial / production processes with Deep Learning methods

Summary:
The scope of this Ph.D. thesis is to examine the possibility of using deep neural networks with architectures, such as those of Time Delay Neural Networks (TDNN) and Long Short-Term Memory (LSTM) for early fault detection in industrial and production processes.  With this thesis, deep learning architectures that will make the produced models capable of use in real production time, will be developed. Also, the use of appropriate metrics for proper result evaluation, as well as feature extraction methods for effective network training, will be investigated. Finally, unsupervised and reinforcement learning algorithms will be utilized for providing solutions in real-life large-scale applications.


PhD Candidate:  Marina B. Delianidi

Supervisor:
Konstantinos I. Diamantaras, Professor IHU

Advisory Committee:
Evangelidis Georgios, Professor University of Macedonia
Sidiropoulos Antonios, Assistant Professor IHU

Title:
Predicting Student Performance Using Time-Dependent Machine Learning Methods and Recommending Educational Content

Summary:
In the field of education and especially in e-learning, through the huge volume of the educational information disseminated on the World Wide Web, the correct recommendation of both a series of courses and educational materials is valuable information for the evolution of students’ educational level. The prediction of students’ knowledge state is the most important information for the successful recommendations of educational content that will contribute to both the improvement and the progress of knowledge state. The aim of the doctoral dissertation is the research of Machine Learning methods for the dynamic assessment of student performance and the development of Recommendation Systems for recommendation educational content to the positive progress of the students’ knowledge state.


PhD Candidate: Pantelis I. Kaplanoglou

Supervisor:
Konstantinos Diamantaras, Professor IHU

Advisory Committee:
George A. Papakostas, Professor IHU
Ignatios Deligiannis, Professor IHU

Title:
Explainable Machine Learning for Intelligent Systems

Summary:
A crucial issue towards widespread application of Machine Learning models is the capability of explaining their functionality and the causes that drive their decisions. The new research area of Explainable Machine Learning offers methods that produce evidence of the system’s behavior in understandable means for humans. By supplying visualizations, metrics and mathematical tools, the understanding on the general functionality of a model, which is based on the formal definition of the method, is expanded to an analytic explanation of its internal characteristics. The non-explainable Deep Neural Networks show increased accuracy compared to explainable models, but with the downside of functioning like black-boxes, to which we provide an input in order to generate an output. Thoughts about widespread use are accompanied with questions regarding reliability, bias and concerns about ethics, physical security. Additionally, there is a lack of trust in them especially in healthcare, pharmaceutical and biomedical sectors. Potential social ramifications have led legislators to establish the “right to explanation” a provision that affects the applicability of state-of-the-art models in products. In parallel, to implement innovative intelligent systems, the experimental implementations of Machine Learning methods need to evolve into software architectures, taking under consideration additional aspects that concern the new sector of Machine Learning Engineering. Research as part of this doctoral thesis focuses on explanatory methods for existing models and attempts to introduce new explainable models, that will be capable to produce consistent predictive results, that humans both anticipate and understand. Secondarily, will produce new standards for machine learning software that provide explanations.


PhD Candidate:  Grigorios S. Katsios

Supervisor:
Efstathios Antoniou, Associate Professor IHU

Advisory Committee:
Alkiviadis Hatzopoulos, Associate Professor IHU
Stavros Vologiannidis, Assistant Professor IHU

Title:
Cooperative Control and Study of the Structural Properties of Singular Multi-Agent Dynamical Systems

Summary:
The present PhD research programme aims to study the structural properties of multi-agent systems, whose components (agents) are themselves singular systems of first, second or higher order. Singular systems arise naturally in the study of dynamical processes which are either subject to algebraic constraints or in cases where the system itself results from the interconnection of smaller components. The aim of the PhD research is to develop results, both at theoretical and application level, by investigating the structural properties of such systems and by extending existing collaborative control techniques in the class of singular cooperative systems.


PhD Candidate:  Lampropoulos Georgios

Supervisor:
Keramopoulos Euclid, Associate Professor, IHU

Advisory Committee:
Diamantaras Konstantinos, Professor, IHU
Evangelidis Georgios, Professor, UoM

Title:
The role of educational technology and gamification in improving education, cognitive and social-emotional development and 21st century skills cultivation: Development and evaluation of virtual and augmented reality applications, artificial intelligence tools and serious games.

Summary:
Nowadays, the rapid technological advances and the digitalization of everyday life have created new educational needs and requirements. The aim of this study is to scrutinize the role of educational technology and gamification in the context of the constantly developing 21st century education and pedagogy. More specifically, this study will examine the way in which the use of emerging technological applications can enrich the contemporary educational process, reinforce wellbeing, promote cognitive and social-emotional development and improve 21st century skills cultivation. Furthermore, it will analyze and present the technologies of augmented reality, virtual reality, artificial intelligence and serious games as well as the contemporary educational approaches of gamification and game-based learning. Through the use of these technologies and approaches and in collaboration with educational communities and institutes intelligent, student-centered and personalized virtual learning environments and applications will be developed and evaluated. Finally, in order to infer valid and reliable conclusions, qualitative and quantitative studies, which will be based on the creation and use of questionnaires and big data analysis, will be included.


PhD Candidate:  Ilias-Nektarios Seitanidis

Supervisor:
Athanasios Iossifides, Associate Professor IHU

Advisory Committee:
Periklis Chatzimisios, Professor IHU
Melpomeni Ioannidou, Associate Professor IHU

Title:
Small cell radio resource management techniques for Internet of Things services improvement in 5G mobile networks.

Summary:
This PhD thesis will study PHY and MAC layer techniques to enhance the Internet of Things services that have differentiated Quality of Service (QoS) requirements in 5G mobile networks. Towards this end, 5G small cell deployments together with novel radio resource management and multiple access techniques will be the main tools to achieve proper network slicing over the air interface resources. The combined management of the radio resources of small cells and normal or macro cells re-enforced by non-orthogonal multiple access (NOMA) techniques will be explored to effectively support multiple data streams of different requirements (e.g. eMBB, URLLC and mMTC use cases) while leaving standard cellular traffic unaffected as much as possible. Spectrum efficiency and energy efficiency will be considered as the main (among others) key performance indicators of the evaluation of the proposed techniques.


PhD Candidate:  Vasileios Stamatis

Supervisor:
Michalis Salampasis, Professor IHU

Advisory Committee:
Konstantinos Diamantaras, Professor IHU
Allan Hanbury, Professor TU Wien

Title:
Applied Intelligence for Federated Patent Search

Summary:
The purpose of this PhD is to contribute in the Federated Patent Search field. The research will start with the results merging sub-processes during distributed patent search in which methods for improving the effectiveness and the efficiency will be evaluated. Existing methods will be optimized and new methods will be discovered like machine learning algorithms for optimizing the results. Furthermore intelligent methods will be implemented in the source selection sub process during distributed patent search. Additionally query expansion is another problem which will be examined and new methods will be created using IPC codes for improving the efficiency of the retrieval. Eventually, the new methods will be embedded in a new system for federated patent search and the system will be tested with real users using user studies.


PhD Candidate:  Charalampos C. Charalampidis

Supervisor:
Panagiotis Adamidis, Professor IHU

Advisory Committee:
Athanasios Iosifidis, Associate Professor IHU
Efkleidis Keramopoulos Associate Professor IHU

Title:
Interoperability enhancement in the «Internet of Things», through utilization of «Semantic Web» technology

Summary:
The main objective of the present doctoral thesis, is the research for Semantic Web (SW) technology utilization in the logical representation of Internet of Things’ (IoT) elements, in order to enhance these elements’ interoperability. The motive for this thesis, is the interoperability deficit, observed among the various IoT elements. The main cause of this deficit is the plethora of protocols and technologies targeting IoT hardware and software, as well as the plethora of semantic models (vocabularies/ontologies), proposed for the logical representation of IoT elements. The interoperability deficit, complicates IoT applications creation without prior knowledge and expertise of the respective technologies, protocols or standards. The goal is to develop know-how in the utilization of Semantic Web technology, for the creation of practical IoT applications, along with IoT elements (“things” under control, IoT management software, IoT user interfaces, etc) featuring enchanced interoperability.